Energy input and output of a rural village in China - the cas of the "Beijing Man village" /District of Beijing

The rapid development of the economy has created an increasing demand for energy in China. The limited resources of fossil energy are a risk for the development of China. Sustainable agriculture like organic farming (Green AAA in China) with biomass energy - as done in developed countries like Germany - is an option to reduce these risks. In China, agriculture is not energy efficient, and the intensive farming is not sustainable. The scientific challenge is to develop sustainable farming systems which can fulfill national food security, food safety and considerable renewable energy production without harming the environment, and are acceptable to the people and the economy. The protection and intelligent utilization of resources is the core of rural village development. To explore the potential of recent Chinese agriculture for the development towards a multi-functional farm for food and energy production, a village in the adjacent area of Beijing has been selected: the “Beijing Man village”. About 1,900 people live in the village and 140 hectares of the 240 hectare total land are available for farming. The major agricultural activity is pork production (capacity of 10,000 pigs per year) and dairy farming (40 dairy cows). In 2004, the energy input and output of this village was evaluated and taken as a basis for a model of sustainable farming for food and biogas production. The study explored that the gross energy production from crops in the “Beijing man village” was about 19,103 GJ/year. It was obvious that the crop production was not sufficient for the feed demand of the animal husbandry (pigs and cows). 60% of the corn used as feed stuff was purchased on the market. The reason was, that the purchasing of corn was cheaper than the own production. The low competitive crop production due to the low efficiency resulted in the decrease of cultivated crop land from 140 ha to 80 ha in the past four years (two harvests per year). On the other hand, there was much more manure produced as suitable and applicable for crop production. Therefore manure was exposed in open air in a pond like waste. This is risky for public hazards like ground water contamination and zoonosis diseases. Therefore the farming system is not sustainable, risky and not efficient. There is a potential of the optimization of the cropping and animal husbandry interaction as well as the development of renewable energy production in the village. The main development chains are the improvement of the energy efficiency of crop production, the reduction of animal husbandry to a sustainable animal-land-ratio and the introduction of biogas production with manure and cropping by-products

Giant Light-Emission Enhancement in Lead Halide Perovskites by Surface Oxygen Passivation.

Surface condition plays an important role in the optical performance of semiconductor materials. As new types of semiconductors, the emerging metal-halide perovskites are promising for next-generation optoelectronic devices. We discover significantly improved light-emission efficiencies in lead halide perovskites due to surface oxygen passivation. The enhancement manifests close to 3 orders of magnitude as the perovskite dimensions decrease to the nanoscale, improving external quantum efficiencies from <0.02% to over 12%. Along with about a 4-fold increase in spontaneous carrier recombination lifetimes, we show that oxygen exposure enhances light emission by reducing the nonradiative recombination channel. Supported by X-ray surface characterization and theoretical modeling, we propose that excess lead atoms on the perovskite surface create deep-level trap states that can be passivated by oxygen adsorption

Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning

Modern deep learning heavily relies on large labeled datasets, which often comse with high costs in terms of both manual labeling and computational resources. To mitigate these challenges, researchers have explored the use of informative subset selection techniques, including coreset selection and active learning. Specifically, coreset selection involves sampling data with both input (\bx) and output (\by), active learning focuses solely on the input data (\bx). In this study, we present a theoretically optimal solution for addressing both coreset selection and active learning within the context of linear softmax regression. Our proposed method, COPS (unCertainty based OPtimal Sub-sampling), is designed to minimize the expected loss of a model trained on subsampled data. Unlike existing approaches that rely on explicit calculations of the inverse covariance matrix, which are not easily applicable to deep learning scenarios, COPS leverages the model's logits to estimate the sampling ratio. This sampling ratio is closely associated with model uncertainty and can be effectively applied to deep learning tasks. Furthermore, we address the challenge of model sensitivity to misspecification by incorporating a down-weighting approach for low-density samples, drawing inspiration from previous works. To assess the effectiveness of our proposed method, we conducted extensive empirical experiments using deep neural networks on benchmark datasets. The results consistently showcase the superior performance of COPS compared to baseline methods, reaffirming its efficacy

Physiological effects of combined NaCl and NaHCO3 stress on the seedlings of two maple species

Salt stress impacts growth and physiological processes in plants, and some plants exposed to salt stress will produce physiological mechanisms to adapt to the new environment. However, the effects of combined NaCl and NaHCO3 stress on the seedlings of Acer species are understudied. In this study, we designed an experiment to measure physiological characteristics by establishing a range of NaCl and NaHCO3 concentrations (0, 25, 50, 75, and 100 mmol L-1) to estimate the compound salt tolerance of Acer ginnala and Acer palmatum. When the concentrations of NaCl and NaHCO3 were 25 mmol L-1, the leaf water content, relative conductivity, malondialdehyde (MDA) content, proline content, soluble sugar content, and chlorophyll did not change (p > 0.05) in two maple seedlings. At concentrations greater than 50 mmol L-1, the relative conductivity and MDA content increased, proline and soluble sugars accumulated, and the potential activity of PS II (Fv/Fo), potential photochemical efficiency of PS II (Fv/Fm), PS II actual photochemical efficiency (Yield), and photosynthetic electron transfer efficiency (ETR) decreased (p < 0.05). The superoxide dismutase (SOD) and catalase (CAT) activities showed the same trend of first increasing and then decreasing (p < 0.05). The peroxidase (POD) activity increased only when concentrations of NaCl and NaHCO3 were 100 mmol L-1, while there was no statistical difference between the other treatments and the control. Therefore, the two maple seedlings adjusted their osmotic balance and alleviated oxidative stress by accumulating proline, soluble sugars and increasing CAT and SOD activities. Further analysis showed that both species are salt tolerant and the salt tolerance of Acer ginnala is better than that of Acer palmatum

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

Funding Information: This work was supported by the National Key R&D Program of China (No.2018YFB-1308400) and partially supported by the Portuguese Agency Fundação para a Ciência e a Tecnologia (FCT), in the framework of project UID/EEA/00066/2020. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.In this paper, the robot grasping for stacked objects is studied based on object detection and grasping order planning. Firstly, a novel stacked object classification network (SOCN) is proposed to realize stacked object recognition. The network takes into account the visible volume of the objects to further adjust its inverse density parameters, which makes the training process faster and smoother. At the same time, SOCN adopts the transformer architecture and has a self-attention mechanism for feature learning. Subsequently, a grasping order planning method is investigated, which depends on the security score and extracts the geometric relations and dependencies between stacked objects, it calculates the security score based on object relation, classification, and size. The proposed method is evaluated by using a depth camera and a UR-10 robot to complete grasping tasks. The results show that our method has high accuracy for stacked object classification, and the grasping order effectively and successfully executes safely.publishersversionpublishe

A Novel Heterogeneous Parallel System Architecture Based EtherCAT Hard Real-Time Master in High Performance Control System

EtherCAT is one of the preferred real-time Ethernet technologies. However, EtherCAT is not applicable in high-end control fields due to real-time constraints. Clock synchronization and cycle time are the most representative limitations. In this paper, a novel Heterogeneous Parallel System Architecture (HPSA) with features of parallel computation and hard real-time is presented. An HPSA-based EtherCAT hard real-time master is developed to significantly improve clock synchronization and shorten cycle time. Traditional EtherCAT masters feature serial processing and run on a PC. This HPSA-based master consists of two parts: EtherCAT master stack (EMS) and EtherCAT operating system (EOS). EMS implements the parallel operation of EtherCAT to realize the shorter cycle time, and EOS brings a hard real-time environment to the HPSA-based master to improve clock synchronization. Furthermore, this HPSA-based master operates on a heterogeneous System-on-a-chip (SoC). EMS and EOS form a heterogeneous architecture inside this SoC to achieve low-latency process scheduling. Experimental results show that in our HPSA-based EtherCAT hard real-time master, the cycle time reaches the sub-50 μs range, and the synchronization error reduces to several nanoseconds. Thus, this HPSA-based master has great application value in high-performance control systems